Pirate Praveen 271ccb9124 New upstream version 16.0.7+ds1

2023-07-09 08:55:56 +05:30

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stage	group	info
Analyze	Product Analytics	To determine the technical writer assigned to the Stage/Group associated with this page, see https://about.gitlab.com/handbook/product/ux/technical-writing/#assignments

Product analytics (Experiment) (ULTIMATE)

Introduced in GitLab 15.4 as an Experiment feature with a flag named cube_api_proxy. Disabled by default.

cube_api_proxy revised to only reference the Product Analytics API in GitLab 15.6.

cube_api_proxy removed and replaced with product_analytics_internal_preview in GitLab 15.10.

product_analytics_internal_preview replaced with product_analytics_dashboards in GitLab 15.11.

FLAG: On self-managed GitLab, by default this feature is not available. To make it available per project or for your entire instance, ask an administrator to enable the feature flag named product_analytics_dashboards. On GitLab.com, this feature is not available. This feature is not ready for production use.

This page is a work in progress, and we're updating the information as we add more features. For more information, see the group direction page.

How product analytics works

Product analytics uses several tools:

Jitsu - A web and app event collection platform that provides a consistent API to collect user data and pass it through to ClickHouse.
ClickHouse - A database suited to store, query, and retrieve analytical data.
Cube.js - An analytical graphing library that provides an API to run queries against the data stored in Clickhouse.

The following diagram illustrates the product analytics flow:

---
title: Product Analytics flow
---
flowchart TB
    subgraph Adding data
        A([SDK]) --Send user data--> B[Analytics Proxy]
        B --Transform data and pass it through--> C[Snowplow]
        C --Pass the data to the associated database--> D([Clickhouse])
    end
    subgraph Showing dashboards
        E([Dashboards]) --Generated from the YAML definition--> F[Dashboard]
        F --Request data--> G[Product Analytics API]
        G --Run Cube queries with pre-aggregations--> H[Cube.js]
        H --Get data from database--> D
        D --Return results--> H
        H --> G
        G --Transform data to be rendered--> F
    end

Enable product analytics

Introduced in GitLab 15.6 behind the feature flag named cube_api_proxy. Disabled by default.

Moved to be behind the feature flag named product_analytics_admin_settings in GitLab 15.7. Disabled by default.

cube_api_proxy removed and replaced with product_analytics_internal_preview in GitLab 15.10.

product_analytics_internal_preview replaced with product_analytics_dashboards in GitLab 15.11.

FLAG: On self-managed GitLab, by default this feature is not available. To make it available per project or for your entire instance, ask an administrator to enable the feature flags named product_analytics_dashboards and product_analytics_admin_settings. On GitLab.com, this feature is not available. This feature is not ready for production use.

To track events in your project applications on a self-managed instance, you must enable and configure product analytics.

Prerequisite:

You must be an administrator of a self-managed GitLab instance.

On the top bar, select Main menu > Admin.
On the left sidebar, select Settings > General.
Expand the Analytics tab and find the Product analytics section.

Select Enable product analytics and enter the configuration values. The following table shows the required configuration parameters and example values:

Name	Value
Configurator connection string	`https://test:test@configurator.gitlab.com`
Jitsu host	`https://jitsu.gitlab.com`
Jitsu project ID	`g0maofw84gx5sjxgse2k`
Jitsu administrator email	`jitsu.admin@gitlab.com`
Jitsu administrator password	`<your_password>`
Collector host	`https://collector.gitlab.com`
ClickHouse URL	`https://<username>:<password>@clickhouse.gitlab.com:8123`
Cube API URL	`https://cube.gitlab.com`
Cube API key	`25718201b3e9...ae6bbdc62dbb`

Select Save changes.

Product analytics dashboards

Introduced in GitLab 15.5 behind the feature flag named product_analytics_internal_preview. Disabled by default.

product_analytics_internal_preview replaced with product_analytics_dashboards in GitLab 15.11.

Each project can have an unlimited number of dashboards. These dashboards are defined using the GitLab YAML schema, and stored in the .gitlab/analytics/dashboards/ directory of a project repository. The name of the file is the name of the dashboard. Each dashboard can contain one or more visualizations (charts), which are shared across dashboards.

Project maintainers can enforce approval rules on dashboard changes using features such as code owners and approval rules. Dashboards are versioned in source control with the rest of a project's code.

View project dashboards

Introduced in GitLab 15.9 behind the feature flag named combined_analytics_dashboards. Disabled by default.

FLAG: On self-managed GitLab, by default this feature is not available. To make it available per project or for your entire instance, ask an administrator to enable the feature flag named combined_analytics_dashboards. On GitLab.com, this feature is not available. This feature is not ready for production use.

To view a list of product analytics dashboards for a project:

On the top bar, select Main menu > Projects and find your project.
On the left sidebar, select Analytics > Dashboards.
From the list of available dashboards, select the dashboard you want to view.

Define a dashboard

To define a dashboard:

In .gitlab/analytics/dashboards/, create a directory named like the dashboard.

Each dashboard should have its own directory.
In the new directory, create a .yaml file with the same name as the directory.

This file contains the dashboard definition. It must conform to the JSON schema defined in ee/app/validators/json_schemas/product_analytics_dashboard.json.
In the .gitlab/analytics/dashboards/visualizations/ directory, create a .yaml file.

This file defines the visualization type for the dashboard. It must conform to the schema in ee/app/validators/json_schemas/product_analytics_visualization.json.

For example, if you want to create three dashboards (Conversion funnels, Demographic breakdown, and North star metrics) and one visualization (line chart) that applies to all dashboards, the file structure would be:

.gitlab/analytics/dashboards
├── conversion_funnels
│  └── conversion_funnels.yaml
├── demographic_breakdown
│  └── demographic_breakdown.yaml
├── north_star_metrics
|  └── north_star_metrics.yaml
├── visualizations
│  └── example_line_chart.yaml

Define a chart visualization

You can define different charts, and add visualization options to some of them:

Line chart, with the options listed in the ECharts documentation.
Column chart, with the options listed in the ECharts documentation.
Data table, with the only option to render links (array of objects, each with text and href properties to specify the dimensions to be used in links). See example).
Single stat, with the only option to set decimalPlaces (number, default value is 0).

To define a chart for your dashboards:

In the .gitlab/product_analytics/dashboards/visualizations/ directory, create a .yaml file. The filename should be descriptive of the visualization it defines.
In the .yaml file, define the visualization options, according to the schema in ee/app/validators/json_schemas/analytics_visualization.json.

For example, to create a line chart that illustrates event count over time, in the visualizations folder create a line_chart.yaml file with the following required fields:

version
title
type
data
options

Funnel analysis

Use funnel analysis to understand the flow of users through your application, and where users drop out of a predefined flow (for example, a checkout process or ticket purchase).

Each product can also define an unlimited number of funnels. Like dashboards, funnels are defined using the GitLab YAML schema, and stored in the .gitlab/analytics/funnels/ directory of a project repository.

Funnel definitions must include the keys name and seconds_to_convert, and an array of steps.

Key	Description
`name`	The name of the funnel.
`seconds_to_convert`	The number of seconds a user has to complete the funnel.
`steps`	An array of funnel steps.

Each step must include the keys name, target, and action.

Key	Description
`name`	The name of the step. This should be a unique slug.
`action`	The action performed. (Only `pageview` is supported.)
`target`	The target of the step. (Because only `pageview` is supported, this should be a path.)

Example funnel definition

The following example defines a funnel that tracks users who completed a purchase within one hour by going through three target pages:

name: completed_purchase
seconds_to_convert: 3600
steps:
  - name: view_page_1
    target: '/page1.html'
    action: 'pageview'
  - name: view_page_2
    target: '/page2.html'
    action: 'pageview'
  - name: view_page_3
    target: '/page3.html'
    action: 'pageview'

Query a funnel

You can query the funnel data with the REST API. To do this, you can use the example query body below, where you need to replace FUNNEL_NAME with your funnel's name.

NOTE: The afterDate filter is not supported. Please use beforeDate or inDateRange.

{
  "query": {
      "measures": [
        "FUNNEL_NAME.count"
      ],
      "order": {
        "completed_purchase.count": "desc"
      },
      "filters": [
        {
          "member": "FUNNEL_NAME.date",
          "operator": "beforeDate",
          "values": [
            "2023-02-01"
          ]
        }
      ],
      "dimensions": [
        "FUNNEL_NAME.step"
      ]
    }
}

Raw data export

Exporting the raw event data from the underlying storage engine can help you debug and create datasets for data analysis.

Because Cube acts as an abstraction layer between the raw data and the API, the exported raw data has some caveats:

Data is grouped by the selected dimensions. Therefore, the exported data might be incomplete, unless including both utcTime and userAnonymousId.
Data is by default limited to 10,000 rows, but you can increase the limit to maximum 50,000 rows. If your dataset has more than 50,000 rows, you must paginate through the results by using the limit and offset parameters.
Data is always returned in JSON format. If you need it in a different format, you need to convert the JSON to the required format using a scripting language of your choice.

Issue 391683 tracks efforts to implement a more scalable export solution.

Export raw data with Cube queries

You can query the raw data with the REST API, and convert the JSON output to any required format.

To export the raw data for a specific dimension, pass a list of dimensions to the dimensions key. For example, the following query outputs the raw data for the attributes listed:

POST /api/v4/projects/PROJECT_ID/product_analytics/request/load?queryType=multi

{
    "query":{
  "dimensions": [
    "TrackedEvents.docEncoding",
    "TrackedEvents.docHost",
    "TrackedEvents.docPath",
    "TrackedEvents.docSearch",
    "TrackedEvents.eventType",
    "TrackedEvents.localTzOffset",
    "TrackedEvents.pageTitle",
    "TrackedEvents.src",
    "TrackedEvents.utcTime",
    "TrackedEvents.vpSize"
  ],
  "order": {
    "TrackedEvents.apiKey": "asc"
  }
    }
}

If the request is successful, the returned JSON includes an array of rows of results.

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